The present invention relates to a device and method for automatic fall detection for elderly people.
Falls are a major risk for the elderly people living independently. The statistics of falls show that approximately one in every three adults 65 years old or older falls each year, and 30% of those falls result in serious injuries. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades several technological solutions for detection of falls were published, but most of them suffer from critical limitations.
The aging of baby boomers has become a social and economic challenge. Due to the maturation of the baby boomers generation, the United Nation predicts that by the year 2035, 25% of the world population will be aged 65 years or older. In the year 2000 this group accounted for 10% of population compared to 6.9% in 1900. In the United States alone, the number of people over the age 65 is expected to hit 70 million by 2030, doubling from 35 million in 2000, and similar increases are expected worldwide. This demographic trend is already posing many social and economic problems. With the aging population comes a necessity to develop more efficient and cost-effective methods of health monitoring and support for elderly people.
Falls and sustained injuries among the elderly are a major problem worldwide, and are the third cause of chronic disability according to the World Health Organization. The proportion of people sustaining at least one fall during one year varies from 28-35% for the age of 65 and over, while falls often signal the “beginning of the end” of an older person's life. The risk of falling increases with age, and in 2 cases out of 3 it happens at home. People that experience a fall event at home, and remain on the ground for an hour or more, usually die within 6 months.
In the past two decades there have been many commercial solutions and academic developments aimed at automatic and non automatic detection of falls.                A. Social alarm: The social alarm is a wrist watch with a button that is activated by the subject in case of a fall event. The main problem with that solution is that the button is often unreachable after the fall especially when the person is panicked, confused, or unconscious.        B. Automatic fall detector: The most popular solutions for automatic detection of falls are the wearable fall detectors that are based on combinations of accelerometers and tilt sensors, for example devices based on a combination of shock and tilt sensors. An alternative uses three accelerometers to obtain the position, speed and acceleration vector of the person. Noury et al. developed a device that is placed under the armpit, and employs two accelerometers and a microcontroller to compute the orientation of the body. A critical disadvantage of those solutions is that the person has to wear the device in the shower, a place with a high occurrence rate of falling, which means both that the device has to be waterproof, and furthermore people prefer not to wear anything while showering. Moreover, these devices produce many false alarms, and old people tend to forget wearing them frequently.        C. Video analysis-based fall detection system: There are a few solutions from recent years that are based on image processing of the person's movement in real-time. One work analyzes the vertical and horizontal speeds during a fall. Another develops a networked video camera system that detects moving objects, extracting features such as object speed and determines if a human fall has occurred.        
Camera based solutions suffer from particular disadvantages such as privacy concerns, (critical to encouraging takeup), and difficulty in effectively monitoring the entire area of a house where falls may take place.
Due to the disadvantages of the existing fall detection techniques, there is a need for a better solution for the elderly fall detection. The idea of floor vibrations was suggested by Alwan et al. M. Alwan, P. Rajendran, S. Kell et al., “A smart and passive floor-vibration based fall detector for elderly,” in Proceedings ICTTA'06, Damascus, Syria, April 2006, pp. 23-28.